A neuro-fuzzy-based fault detection and fault tolerance methods for industrial robotic manipulators

Abstract

Fault tolerance is increasingly important in industrial robots. The ability to detect and tolerate failures allows robots to effectively cope with internal failures and continue performing designated tasks without the need for immediate human intervention. To tolerate hardware failures, a set of fault tolerance algorithms are written for each component. These processes are responsible for detecting faults in their respective component and minimising the impact of the failure on the robot's performance. This work presents new intelligent neuro-fuzzy fault detection algorithms, which detect failures in robot components using analytical redundancy relations. An intelligent fault tolerance framework is proposed in which a fault component database or rule base and the detection algorithms work together to detect and tolerate sensor or motor failures in a robot system. Motor faults as well as sensor faults are considered. The Scorbot ER 5u plus model was simulated in robotics toolbox for MATLAB using the neuro-fuzzy algorithms.